Top Research Papers on Data Mining

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the a...

Focusing on the use of DL to analyse patterns in data from diverse biological domains, different DL architectures’ applications to these data are investigated and a meta-analysis has been performed and the resulting resources have been critically analysed.

A novel transfer learning strategy to address problems of small or insufficient data by fusing the images obtained from simulating the physical mechanism of grain formation and the “image style” information in real images to generate synthetic data.

A brief introduction to big data and BDM is provided and the precautions for the utilization of BDM in the mining industry are outlined, and a future in which a global database project is established and big data is used together with other technologies supported by government policies and following international standards is envisioned.

An efficient algorithm for the task of HUSP mining with UL-list (HUSP-ULL), which utilizes a lexicographic $q$ -sequence (LQS)-tree and a utility-linked (UL)-list structure to quickly discover HUSPs.

The state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, are investigated.

A comprehensive literature review is presented to provide an overview of how machine learning techniques can be applied to realize manufacturing mechanisms with intelligent actions and points to several significant research questions that are unanswered in the recent literature having the same target.

Experimental analysis indicates that the proposed work is more successful in terms of attack resistance, scalability, execution speed and accuracy when compared with other algorithms that are used for privacy preservation.

This paper explores the feasibility of modeling user personality based on a proposed set of features extracted from the Facebook data, and explores the suitability and performance of several classification techniques.

Abstract The area used for mineral extraction is a key indicator for understanding and mitigating the environmental impacts caused by the extractive sector. To date, worldwide data products on mineral extraction do not report the area used by mining activities. In this paper, we contribute to filling this gap by presenting a new data set of mining extents derived by visual interpretation of satellite images. We delineated mining areas within a 10 km buffer from the approximate geographical coordinates of more than six thousand active mining sites across the globe. The result is a global-scale ...

This chapter outlines current challenges of accessing, analyzing, and using big data and argues that addressing these challenges is worthwhile given the potential benefits of mining big data in education.

The role of emotional analysis in education from four levels: document level, sentence level, entitylevel, and aspect level, and the role of AI in sentiment analysis with methodologies like machine learning, deep learning, and transformers are discussed.

The ease of use and innovative features of lipidr, an open-source R/Bioconductor package for data mining and analysis of lipidomics datasets, are expected to allow the lipidomics research community to gain novel detailed insights from lipidomics data.

Data mining is the computer based process of analyzing huge sets of data and then extracting the meaning of the data. Data mining tools predict future trends, allowing business to make positive, knowledge-driven decisions. The huge amounts of data generated by traditional methods for prediction of heart disease are too complex and voluminous to be processed and analyzed. Data mining provides the technologies to transform these huge sets of data into useful information for decision making. Data mining techniques takes less time for the prediction of the disease with more accuracy. In this paper...

This work has introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.

An overview of information found in EHR systems and their characteristics that could be utilized for secondary applications is provided and can serve as a primer for researchers to understand the use of EHRs for data mining and analytics purposes.

A systematic and detailed review of various data mining techniques employed in the large and small scale IoT applications to formulate an intelligent environment and an overview of cloud-assisted IoT Big data mining system are presented to better understand the importance of data mining for an IoT environment.

The current state of the art in data mining in education is provided by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, themain objectives, and the future trends in this research area.

This work investigates the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks.

Data mining is an important tool in science, engineering, industrial processes, healthcare, business, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high performance and principled analysis techniques and algorithms, based on sound theoretical and statistical foundations. These techniques in turn require implementations that are carefully tuned for performance; powerful visualization technologies; interface systems that are usable by scientists, engineers, and physicians as well as researchers; and infrast...